4 Powerful Use Cases for Data Science in Finance

There are a plethora of success stories demonstrating how major financial players capitalise on their data. But what's the significance of data science services in finance now, in particular?

The coronavirus pandemic and the global measures that have followed have created an economic storm. The financial sector stands at the front line of a growing credit crisis, with banks trying to manage disruption and maintain strict compliance amid social distancing guidelines which are at odds with their processes. Then there are the extraordinarily low interest rates and increasingly cash-insecure consumers to contend with. Some of the biggest banking challenges posed by the pandemic are:

• Prioritising resources to cover the most critical business processes. Like many industries, the banking sector has found itself scrambling for answers and slow to make decisions on resourcing capacity, because it lacked an adequate data repository.
• Delivering financial services off-site. Even though online banking software is a common solution these days, some essential financial operations can only be done on-site.
• Dealing with the rising number of fraud cases. There have been numerous cases of critical data theft since COVID-19 first appeared. With rigorous data analysis, suspicious transactions could have been identified sooner and monetary fraud prevented.

To navigate the immediate obstacles, financial institutions must assess short-to-medium-term financial risks and adapt to new ways of operating in a post-pandemic world. Data science can be a powerful tool in finance, aiding risk management and continuity planning so that the industry is better prepared when the next challenge arises.

4 ways to harness data science within finance

A recent report from the World Economic Forum predicts that 463 exabytes of data will be generated daily by 2025. That‘s equal to about 10 million bluray disks a day, with an almost incomprehensible amount of actionable insights. Here are four key examples of how insurance, banking and investment companies can use data science to innovate the financial field.

1. Detect and prevent fraud

According to the American Bankers Association, banking institutions prevented $22 billion worth of fraudulent transactions in 2018. Now, using solutions powered by machine learning technologies, the finance industry is aiming at real-time fraud detection to minimise losses.

Machine learning enables the creation of algorithms that can learn from data, spot any unusual user behaviour, predict risks, and automatically notify financial companies of a threat. Data science helps banks recognise:

• Fake insurance claims. With the help of machine learning algorithms, data provided by insurance agents, police, or clients can be analysed to spot inconsistencies more accurately than with manual checks.
• Duplicate transactions and insurance claims. Duplicated invoices or claims aren’t always sinister, but machine learning algorithms can distinguish between an accidental click and a premeditated fraud attempt, thus preventing financial losses.
• Account theft and suspicious transactions. Algorithms can analyse a user’s routine transactional data, then any suspicious activity can be flagged and verified by the card owner.

2. Manage customer data more efficiently

Financial institutions are responsible for managing vast amounts of customer data – transactions, mobile interactions and social media activity. This information can be categorised as structured, semi-structured or unstructured – the latter posing a real challenge when it comes to processing.

Employing data science within finance helps companies manage and store customers’ data far more efficiently. Firms can boost profits using AI-driven tools and technologies such as natural language processing (NLP), data mining and text analytics, while machine learning algorithms analyse data, identify valuable insights and suggest better business solutions.